Machine learning guided engineering of conditional split inteins for regulated protein splicing in mammalian cells

Abstract

Inteins are proteins that excise themselves from precursor proteins and connect the flanking polypeptides with a peptide bond. Split inteins consist of two independently translated fragments that must associate to become splice-competent. They can be used for diverse post-translational protein modifications. Using the ML Int&in algorithm, we predicted unnatural split sites in two of the fastest and most efficient split inteins, gp41-1 and NrdJ-1, to generate functional variants with fragments of reduced mutual affinity. We harness this feature to create conditional versions of these inteins by controlling the physical proximity of the intein fragments with a light-inducible heterodimerization system. The resulting light-activatable gp41-1 and NrdJ-1 inteins enabled blue light-dependent control of Cre recombinase activity in mammalian cells, which we exploited to spatially control apoptosis via localized expression of truncated BID (tBID) and caspase-8. This work highlights the versatility of Int&in for designing conditional inteins for precise spatiotemporal protein regulation.